Search Results for author: Xuchen You

Found 6 papers, 1 papers with code

Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels

no code implementations26 Mar 2023 Xuchen You, Shouvanik Chakrabarti, Boyang Chen, Xiaodi Wu

In this work, we study the dynamics of QNNs and show that contrary to popular belief it is qualitatively different from that of any kernel regression: due to the unitarity of quantum operations, there is a non-negligible deviation from the tangent kernel regression derived at the random initialization.


A Convergence Theory for Over-parameterized Variational Quantum Eigensolvers

no code implementations25 May 2022 Xuchen You, Shouvanik Chakrabarti, Xiaodi Wu

The Variational Quantum Eigensolver (VQE) is a promising candidate for quantum applications on near-term Noisy Intermediate-Scale Quantum (NISQ) computers.

Exponentially Many Local Minima in Quantum Neural Networks

no code implementations6 Oct 2021 Xuchen You, Xiaodi Wu

Specifically, we show for typical under-parameterized QNNs, there exists a dataset that induces a loss function with the number of spurious local minima depending exponentially on the number of parameters.

Quantum exploration algorithms for multi-armed bandits

1 code implementation14 Jul 2020 Daochen Wang, Xuchen You, Tongyang Li, Andrew M. Childs

Identifying the best arm of a multi-armed bandit is a central problem in bandit optimization.

Multi-Armed Bandits

On Second-Order Group Influence Functions for Black-Box Predictions

no code implementations ICML 2020 Samyadeep Basu, Xuchen You, Soheil Feizi

Often we want to identify an influential group of training samples in a particular test prediction for a given machine learning model.

BIG-bench Machine Learning Test

Guaranteed Simultaneous Asymmetric Tensor Decomposition via Orthogonalized Alternating Least Squares

no code implementations25 May 2018 Furong Huang, Jialin Li, Xuchen You

We propose a Slicing Initialized Alternating Subspace Iteration (s-ASI) method that is guaranteed to recover top $r$ components ($\epsilon$-close) simultaneously for (a)symmetric tensors almost surely under the noiseless case (with high probability for a bounded noise) using $O(\log(\log \frac{1}{\epsilon}))$ steps of tensor subspace iterations.

Tensor Decomposition

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